class_docstr: Wraps the weight so that in can be sliced with a ``multidim_slice``.
  The value within the slice can be instances of :class:`MaybeWeighted`.
  
  Examples
  --------
  >>> weight = conv2d.weight
  >>> Slicable(weight)[:MaybeWeighted({32: 0.4, 64: 0.6})]
  Tensor of shape (64, 64, 3, 3)
code: def __getitem__(self, index: slice_type | multidim_slice | Any) -> T:
      if not isinstance(index, tuple):
          index = (index,)
      index = cast(multidim_slice, index)
  
      # Get the dict value in index's leafs
      # There can be at most one dict
      leaf_dict: dict[int, float] | None = None
      for maybe_weighted in _iterate_over_multidim_slice(index):
          for d in maybe_weighted.leaf_values():
              if isinstance(d, dict):
                  if leaf_dict is None:
                      leaf_dict = d
                  elif leaf_dict is not d:
                      raise ValueError(
                          "There can be at most one distinct dict in leaf values."
                      )
  
      if leaf_dict is None:
          # in case of simple types with no dict
          res_index = _evaluate_multidim_slice(index)
      else:
          # there is a dict, iterate over dict
          res_index = []
          for val, wt in leaf_dict.items():
              res_index_item = _evaluate_multidim_slice(
                  index, lambda _: val
              )
              res_index.append((res_index_item, wt))
  
      return _slice_weight(self.weight, res_index)
  
imports: from __future__ import annotations
  import operator
  from typing import Callable, Iterator, TypeVar, Any, Optional, Tuple, Union, List, Dict, Generic, cast
  import numpy
  import torch
label_desc: Use image node_id 2 for calling the Slicable obj's underlying member method code with example usage: obj.__getitem__(index) and returns: _slice_weight